Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network

Gandhi, Deep; Thakur, Govind; Bari, Pranit; Deulkar, Khushali

doi:10.1201/9781003133681-18

Cited by 1 publication

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“…It is vital to build an effective talent evaluation system and to improve the management of talent assessment in order to identify competent management accountants for training on campus or through social media. In turn, they are able to provide short-term intensive training and evaluation, boost liquidity among appropriate accountants, and minimise the rigidity of the accounting sector within the organization [ 17 ]. As a result of the growth of new media, there has been a substantial shift in the rate at which information is disseminated as well as the impact that information has on society as a whole in the information age.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents

Liu¹,

Li²,

Zhang³

2022

Computational Intelligence and Neuroscience

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

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Section: Introductionmentioning

confidence: 99%